def get_reward(self, obs, acs):
sess = U.get_session()
if len(obs.shape) == 1:
obs = np.expand_dims(obs, 0)
if len(acs.shape) == 1:
acs = np.expand_dims(acs, 0)
feed_dict = {self.generator_obs_ph: obs, self.generator_acs_ph: acs}
reward = sess.run(self.reward_op, feed_dict)
return reward
def add_all_summary(self, writer, values, iter):
# Note that the order of the incoming ```values``` should be the same as the that of the
# ```scalar_keys``` given in ```__init__```
if np.sum(np.isnan(values) + 0) != 0:
return
sess = U.get_session()
keys = self.scalar_summaries_ph + self.histogram_summaries_ph
feed_dict = {}
for k, v in zip(keys, values):
feed_dict.update({k: v})
summaries_str = sess.run(self.summaries, feed_dict)
writer.add_summary(summaries_str, iter)
def get_reward(self, obs, acs, last_acs):
sess = U.get_session()
# if len(obs.shape) == 1:
# obs = np.expand_dims(obs, 0)
# if len(acs.shape) == 1:
# acs = np.expand_dims(acs, 0)
one_hot_acs = []
if type(acs) is np.ndarray:
depth = acs.size
one_hot_acs = np.zeros((depth, 524))
one_hot_acs[np.arange(depth), acs] = 1
else:
# one_hot_acs = tf.one_hot(indices, depth)
one_hot_acs = np.zeros(524)
one_hot_acs[acs] = 1
one_hot_acs = [one_hot_acs]
one_hot_last_acs = []
if type(last_acs) is np.ndarray:
depth = last_acs.size
one_hot_last_acs = np.zeros((depth, 524))
one_hot_last_acs[np.arange(depth), last_acs] = 1
else:
one_hot_last_acs = np.zeros(524)
one_hot_last_acs[last_acs] = 1
one_hot_last_acs = [one_hot_last_acs]
feed_dict = {
self.generator_obs_ph: obs,
self.generator_acs_ph: one_hot_acs,
self.generator_last_action_ph: one_hot_last_acs
}
# g_acc = sess.run(self.generator_acc, feed_dict)
# reward = 0
# if g_acc > 0.99:
# reward = np.log(1-g_acc+1e-5) # give negative reward
# else:
reward = sess.run(self.reward_op, feed_dict)
if acs in [0, 1, 2, 3, 4, 274]:
reward /= 2
# if reward < 0.01:
# # give negative reward
# reward = 20 * reward - 0.2
# if np.allclose(reward, 0):
# reward = -1
# if reward == 0:
# print('reward should not equal to 0!!!!!')
return reward
def get_reward(self, trajs, trajs_len, dropout_keep_prob=1.0):
sess = U.get_session()
if len(trajs.shape) == 2:
trajs = np.expand_dims(trajs, 0)
if len(np.shape(trajs_len)) == 0:
trajs_len = np.expand_dims(trajs_len, 0)
feed_dict = {
self.generator_traj_ph: trajs,
self.generator_traj_seq_len: trajs_len,
self.dropout_keep_prob: dropout_keep_prob
}
rewards = sess.run(self.rewards_op, feed_dict)
return rewards